—Distance education has been rapidly becoming widespread all around the world. This rapid growth brings along both success and failure. One of the causes of failure in distance education is the lack of student observation, which is a part of traditional education. We can overcome this deficit through an analysis of logs kept in the learning management system. These logs make it possible to estimate academic performance of a student attending to distance education. The instructor having such information can take precautions in order to prevent failure. In this study, a mathematical model has been formed by using Fuzzy Logic. In order to enhance the success of model, fuzzy logic rules have been optimized by using genetic algorithm. Three different variableshave been introduced to the proposed model. These are recency, stating the number of days that pass from the date of registering course contents until the date of student’s admission to the system; frequency, stating the frequency of logging on to the system and monetary, stating the period of time spent online on the system. End of term scores of students have been estimated on the basis of the first 6-week data and then the results have been verified by the real scores.
—Academic performance, fuzzy logic, genetic algorithm, rule optimization.
O. Yildiz and F. D. Kentli are with the Department of Informatics, Yildiz Technical University, Istanbul, Turkey (e-mail: firstname.lastname@example.org, email@example.com).
A. Bal is with the Department of Electronics and Communications Engineering, Yildiz Technical University, Istanbul, Turkey (e-mail: firstname.lastname@example.org).
S. Gulsecen is with the Department of Informatics, Istanbul University, Istanbul, Turkey (e-mail: email@example.com).
Cite: Osman Yildiz, Abdullah Bal, Sevinc Gulsecen, and Fulya D. Kentli, "Rules Optimization Based Fuzzy Model for Predicting Distance Education Students’ Grades," International Journal of Information and Education Technology vol. 4, no. 1, pp. 59-62, 2014.